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. 2023 Sep:238:105512.
doi: 10.1016/j.cognition.2023.105512. Epub 2023 Jun 16.

The transposed word effect is consistent with serial word recognition and varies with reading speed

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The transposed word effect is consistent with serial word recognition and varies with reading speed

Jannat Hossain et al. Cognition. 2023 Sep.

Abstract

The scientific study of reading has long been animated by questions of parallel vs. serial processing. Do readers recognize words serially, adding each one sequentially to a representation of the sentence structure? One fascinating phenomenon to emerge from this research is the transposed word effect: when asked to judge whether sentences are grammatical, readers often fail to notice grammatical errors caused by transposing two words. This effect could be evidence that readers recognize multiple words in parallel. Here we provide converging evidence that the transposed word effect is also consistent with serial processing because it occurs robustly when the words in each sentence are presented serially. We further investigated how the effect relates to individual differences in reading speed, to gaze fixation patterns, and to differences in difficulty across sentences. In a pretest, we first measured the natural English reading rate of 37 participants, which varied widely. In a subsequent grammatical decision task, we presented grammatical and ungrammatical sentences in two modes: one with all words presented simultaneously, and the other with single words presented sequentially at each participant's natural rate. Unlike prior studies that used a fixed sequential presentation rate, we found that the magnitude of the transposed word effect was at least as strong in the sequential presentation mode as in the simultaneous mode, for both error rates and response times. Moreover, faster readers were more likely to miss transpositions of words presented sequentially. We argue that these data favor a "noisy channel" model of comprehension in which skilled readers rely on prior knowledge to rapidly infer the meaning of sentences, allowing for apparent errors in spatial or temporal order, even when the individual words are recognized one at a time.

Keywords: Parallel processing; Reading; Serial processing; Transposed word effect; Word recognition.

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Conflict of interest statement

Declaration of Competing Interest None.

Figures

Figure 1:
Figure 1:
(A) Example simultaneous presentation trial in the grammatical decision task, with a transposed stimulus. (B) Example sequential presentation trial. (C) Smoothed distributions of reading rates in words per second. The printed numbers are the means, which are also represented by the horizontal lines. The short vertical lines are 95% bootstrapped CIs. For the sequential presentation mode, these data are presentation rates set to match each individual’s reading rate from the pretest. For the simultaneous condition, reading rates are averaged across grammaticality conditions. Each trial’s rate is the total time spent reading the stimulus, divided by the number of words presented (5) plus the number of words that were fixated during regressions (glances leftwards in the text). (D) The correlation between individual participant reading rates, in the sequential mode (x-axis) vs simultaneous mode (y-axis).
Figure 2:
Figure 2:
Performance in the grammatical decision task. (A) Mean error rates and (B) correct RTs in each condition. Error bars are +/− 1 SEM. Asterisks indicate significant differences between the transposed and control conditions: *p<0.05; **p<0.01, **p<0.001. (C) Smoothed distributions of individual subject transposed word effects (TW effects) on error rates: transposed - control. The horizontal lines and printed numbers represent the mean effects, with 95% CIs as vertical bars. (D) Smoothed distributions of TW effects on correct RTs, format as in (C). (E) Scatter plot of individual TW effects on error rates, showing the correlation between TW effects in the simultaneous vs. sequential modes. The solid diagonal line is a linear regression. The dashed diagonal line has a slope of 1 and is where all the points would fall if the TW effect was equally large in the two presentation modes. The dots that fall above this diagonal line are participants who had a greater TW effect in the sequential mode. (F) Data as in panel (E), but for correct RTs.
Figure 3.
Figure 3.
The transposed word effect varies across slow and fast readers. (A) For the simultaneous presentation mode: individual participant error rates for transposed (green) and control sentences (blue) as a function of their reading rate. Correlation coefficients are printed for each sentence type, and the solid lines are linear regressions. (B) Similar format, but for the simultaneous mode. The x-axis is the participant's presentation rate (which was well matched to their simultaneous mode reading rate, the x-axis in panel A). There was a strong correlation only for transposed sentences in the sequential mode. (C) This plot shows how the transposed word effect (TW effect; transposed – control) differs between slow and fast readers, for each presentation mode (light vs dark bars). Participants were assigned to the slow or fast group by a median split of reading speeds in the pretest. Error bars are +/− 1 SEM. ** p=0.002.
Figure 4:
Figure 4:
The influence of eye movement patterns on error rates in the simultaneous presentation mode. Error bars are +/− 1 SEM. Asterisks: * p<0.05; **p<0.01. The numbers printed on the bars indicate the proportion of trials with that sentence type that fall into the second bin (e.g., 58% of grammatical trials were in the “fast” reading bin). (A) For each subject we first calculated their median reading rate across all trials, then split the trials of each condition into two bins: “slow” (rate <= median) and “fast” (rate > median). Each trial’s reading rate was, as before, calculated as the number of words read (five plus the number re-read) divided by total reading time. (B) Trials are sorted depending on whether both words 3 and 4 in the sentence were fixated in the first pass through the sentence, or at least one of them was skipped. (C) Trials are sorted depending on whether the subject made at least one regressive eye movement. Collapsing across sentence types, there is a significant difference in accuracy between trials with and without a regression (see text).
Figure 5:
Figure 5:
Associations between response time and accuracy. The left column is for the simultaneous presentation mode, and the right column for the sequential mode. Panels (A) and (B) plot across-subject mean accuracy in four bins of trials sorted according to response time. Error bars are +/− 1 SEM. In panels (C) and (D), each dot is an individual sentence. The x-axis is the across-subject mean of z-scored RTs for each sentence. The y-axis is the proportion of subjects that judged each sentence correctly. The solid lines are best-fitting linear regressions. The text in the lower right corners reports the correlation coefficients for each sentence type (G=grammatical; C=control, T=transposed). * p<0.05; **p<0.01.

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